In the context of physics-based vision there is in fact a compelling motivation to study polarization vision--polarization affords a more general description of light than does intensity, and can therefore provide a richer set of descriptive physical constraints for the interpretation of an imaged scene. As intensity is the linear sum of polarization components, intensity images physically represent reduced polarization information. Because the study of polarization vision is more general than intensity vision, there are polarization cues that can immensely simplify some important visual tasks (e.g., region and edge segmentation, material classification, etc. . . . ) which are more complicated or possibly infeasible when limited to using intensity and color information. A detailed description of a variety of polarization-based vision methods are contained in L. B. Wolff. Surface orientation from polarization images. In Proceedings of Optics, Illumination and Image Sensing for Machine Vision II, Volume 850, pages 110-121, Cambridge, Mass., November 1987. SPIE; L. B. Wolff. Polarization-based material classification from specular reflection. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 12(11):1059-1071, November 1990; L. B. Wolff and T. E. Boult. Constraining object features using a polarization reflectance model. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 13(7):635-657, July 1991; L. B. Wolff. Polarization Methods in Computer Vision. PhD thesis, Columbia University, January 1991; T. E. Boult and L. B. Wolff. Physically-based edge labeling. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Maui, June 1991.
A criticism that has sometimes been leveled at polarization-based vision methods is the inconvenience of obtaining polarization component images by having to place a linear polarizing filter in front of an intensity CCD camera and mechanically rotating this filter by hand or by motor into different orientations. This inconvenience is a result of commercially available camera sensors being geared towards taking intensity images instead of polarization images. There are considerable advantages to building a camera sensor geared towards doing polarization vision, capable of taking polarization images without external mechanical manipulation of a filter. There already exist polarization-based vision methods that can significantly benefit a number of application areas such as aerial reconnaissance, autonomous navigation, inspection, and, manufacturing and quality control. A polarization camera would make polarization-based vision methods more accessible to these application areas and others. It should be fully realized that as intensity is a compression of polarization component information, a polarization camera can function as a conventional intensity camera, so that intensity vision methods can be implemented by such a camera either alone, or, together with polarization-based vision methods. As intensity-based methods are physical instances of polarization-based methods, a camera sensor geared towards polarization vision does not in any way exclude intensity vision, it only generalizes it providing more physical input to an automated vision system! Adding color sensing capability to a polarization camera makes it possible to sense the complete set of electromagnetic parameters of light incident on the camera.
The present invention in a preferred embodiment involves a polarization viewer that does not require any external mechanical manipulation of a filter to form a transmitted radiance sinusoid. With the sinusoid, polarization states can be mapped into hue, saturation and intensity which is a very convenient representation for a polarization image.